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Erschienen in:
Software Implementation Methodology of Intelligent Information Systems of Learning and Knowledge Control (IISLKC) Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Choquet Integral with Interval Type 2 Sugeno Measures as an Integration Method for Modular Neural Networks

verfasst von : Gabriela E. Martínez, Olivia Mendoza, Juan R. Castro, Patricia Melin, Oscar Castillo

Erschienen in: Recent Developments and New Direction in Soft-Computing Foundations and Applications

Verlag: Springer International Publishing

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Abstract

In this paper, a new method for response integration, based on the Choquet integral with interval type 2 Sugeno measures, is presented. Type 1 and interval type 2 fuzzy systems for edge detection based on the Sobel and morphological gradient are used, which is a preprocessing system applied to the training data for better performance in the modular neural network. Fuzzy Sugeno measures are represented by an interval type 2 fuzzy system. The Choquet integral is used as a method to integrate the outputs of the modules of the modular neural networks (MNN). A database of faces was used to perform the preprocessing, the training, and the combination of information sources of the MNN.

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Vorheriges Kapitel Bipolarity and Multipolarity in Aggregation Structures
Nächstes Kapitel Fuzzy Logic Ideas Can Help in Explaining Kahneman and Tversky’s Empirical Decision Weights
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Metadaten
Titel
Choquet Integral with Interval Type 2 Sugeno Measures as an Integration Method for Modular Neural Networks
verfasst von
Gabriela E. Martínez
Olivia Mendoza
Juan R. Castro
Patricia Melin
Oscar Castillo
Copyright-Jahr
2016
Buch
Recent Developments and New Direction in Soft-Computing Foundations and Applications

Print ISBN: 978-3-319-32227-8

Electronic ISBN: 978-3-319-32229-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-32229-2_6

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